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Record W3197836228 · doi:10.1111/mec.16144

Environmental versus extra‐organismal DNA

2021· letter· en· W3197836228 on OpenAlexaff
Jan Pawłowski, Laure Apothéloz‐Perret‐Gentil, Florian Altermatt

Bibliographic record

VenueMolecular Ecology · 2021
Typeletter
Languageen
FieldEnvironmental Science
TopicEnvironmental DNA in Biodiversity Studies
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsTerminologyCLARITYBiologyContext (archaeology)Environmental DNAPrivilege (computing)EcologyEvolutionary biologyBiodiversityPolitical sciencePaleontologyLawLinguistics

Abstract

fetched live from OpenAlex

We are very pleased that our opinion paper “Environmental DNA: What's behind the term?” (Pawlowski et al., 2020) stimulated a lively discussion and we are grateful for the comments on proposed terminology (Rodriguez-Ezpeleta et al., 2021). The clarity of scientific terms is essential for both fundamental and applied research and any debate on this issue is very important, especially in the early days of a new field. A major requirement of clarity is that terminology refers to measurable and implementable classifications. To recall the context of this debate, the aim of our paper was to restore a broad definition of environmental DNA (eDNA) as referring to all organisms present in environmental samples, including both macrobial and microbial organisms (Taberlet et al., 2012). We are very glad that our proposition to adopt a broad definition of eDNA was accepted by Rodriguez-Ezpeleta et al. (2021). However, we cannot agree with their opinion that our two-level terminology is oversimplistic. Our terminology refers to the two basic steps of any eDNA metabarcoding study, defining first the material (i.e., environmental sample) taken for DNA extraction and second the taxonomic group targeted by PCR (polymerase chain reaction). This may be simple but is also directly and clearly applicable, thereby clarifying the aims and targets of eDNA studies in general. We privilege the choice of material and method over the potential outputs of a study. According to our view, even if sediment samples are used as a source of information about present or past surface plankton, these are still sediment eDNA studies (e.g., Monchamp et al., 2018; Morard et al., 2017). Similarly, if taxon-specific PCR primers also amplify other taxa, the target taxon should be mentioned, rather than PCR byproducts (e.g., Mächler et al., 2019). It is important to highlight that our proposed classification remains open to a more detailed specification of the study of eDNA. We think that targeting extra-organismal DNA corresponds to such complementary information and this information can be included in the description of the study. As correctly emphasized by Rodriguez-Ezpeleta et al. (2021), the ecological interpretation of extra-organismal DNA data must consider many factors specific to this type of DNA. Nevertheless, in practice such a distinction at best concerns only those organisms over a certain size. As shown in figure 1 of Rodriguez-Ezpeleta et al. (2021), there is overlap over at least six orders of magnitude in size between intra- and extra-organismal DNA, and even they conclude that “it is currently impractical to separate and independently analyse organismal and extra-organismal DNA.” Given the continuous occurrence and transition of DNA from living organisms, to within tissues or cells (living or dead), to organelles and truly free DNA, we also think such separation is methodologically challenging if not impossible, and thus not directly applicable. While smaller-sized organisms (microbes or small animals such as rotifers) may be indeed often sampled in their living state, they can still also be recorded through DNA from degraded cells or organisms. By analogy, large organisms, such as mussels, may be largely recorded by extra-organismal DNA, but the occurrence of veliger larvae in water eDNA samples may go unnoticed and not be separable. Indeed, the complex mixture of different-origin (or “types” of) DNA may be difficult to resolve, and we recommend sticking to the directly applicable, technical terminology proposed by us. We fully understand the importance of eDNA for the detection and monitoring of aquatic vertebrates, especially fish and amphibians, and we recognize the need to assess the specific biases and types of noise related to its primarily extra-organismal character. In this particular case, the origin of eDNA is selbstverständlich, so it is unlikely that the meaning of the term might lead to confusion. We thank the three reviewers for their thoughtful comments.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.016
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.013
Scholarly communication0.0080.010
Open science0.0020.004
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0060.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.186
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations6
Published2021
Admission routes1
Has abstractyes

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